The potential of open learner models to promote active thinking by enhancing self‐regulated learning in online higher education learning environments

Abstract

The main aim of the present study is to assess whether the open learner model (OLM) is capable of promoting students' active thinking by enhancing their self‐regulation in online higher education learning environments. To this aim, we systematically reviewed the literature of the last three decades and found 67 articles, of which only a sample of 15 were considered. Based on the findings, we performed a narrative analysis of the studies concerning technological features of OLMs that cater to the three main aspects concerning self‐regulated learning, namely, cognition, metacognition and motivation. Our analysis of the literature confirmed that these three aspects are all subject to some measure of influence. In mutually interacting, these three components support learners to reach a better understanding of their learning process. Specifically, it seems that mostly all three type of OLMs, inspectable, negotiable, and co‐operative, with simple and complex graphical presentation of their learner models and capacity to colour‐code and compare—alike appear optimal for augmenting cognition, metacognition, and motivation. They seemingly do so through offering a wealth of techniques pertaining to knowledge, difficulties, and misconception visualization. The results presented suggest that OLMs have a positive impact on learners' active thinking regarding their learning process. Practitioner NotesWhat is already known about this topic Several educational environments have been enhanced with open learner models (OLMs) with the aim of supporting learners. OLM seems to be a potential avenue for improving higher education by supporting learners' active thinking about their learning process by enhancing self‐regulated learning. Despite growing interest in OLMs, there has yet to be any effort to systematically review current research studies investigating their potential to promote self‐regulated learning. What this paper adds All type of OLMs, with their varied visualization features, to some extent are useful for promoting cognitive, metacognitive, and motivational components of self‐regulated learning. In mutually interacting, the three components of self‐regulated learning support learners to reach a better understanding of their learning process, and thereby promoting active thinking. Implications for practice and/or policy We suggest that practitioners must first grasp the possibilities for the cultivation of self‐regulated learning contained in the particular features of each OLM, and then add this insight to their own expertise in order to best integrate them for supporting students' active thinking. While a number of specific features of OLMs seem pertinent to cultivating one particular element of self‐regulated learning, there remains the chance that, through the interaction of cognitive, metacognitive, and motivational tactics, these features could in fact make a more holistic and general contribution. This supports learners to move from active‐in‐behaviour to active‐in‐thinking activities in digital learning environments.

Mining Educational Data to Predict Students’ Performance through Procrastination Behavior

“…There have been several studies revolving regarding the fundamental success or failure factors of online learning. For example, Azevedo et al [ 5 ] and Hooshyar et al [ 6 ] highlighted several challenges, e.g., self-pacing and self-regulation, more effort, rapid learning, etc., that usually hinder students from adapting to LMSs. Among all, procrastination—especially a tendency to put off completing tasks—and a lack of time are underlined by students as the main reasons when asked about their failure or dropping out of an online course (e.g., [ 7 , 8 ]).…”

Does Inquiry-based Education Using Robots Have an Effect on Learners’ Inquiry Skills, Subject Knowledge and Skills, and Motivation?

“…It means that we need to analyze and revise the procedure of applying the learning scenarios so that students focus more on conceptualizing their activities. Following the ideas of open learner modeling, we suggest making the learning process and progress more explicit to the learners [27][28] [29] [30]. In this case, learning with robots could support the improvement of learning skills, a gap identified by Pedaste and Leijen [8] in using innovative technologies in education.…”

Analisis Kemampuan Self Regulation Siswa pada Pembelajaran Sains saat PJJ Online di Era Pandemi Covid-19

“…Komponen kognitif secara spesifik terdiri dari kegiatan mengoperasikan informasi yang telah diperoleh, dimanipulasi dan digunakan dalam proses pembelajaran (Hooshyar, Kori, Pedaste, & Bardone, 2019). Komponen kognitif merupakan kemampuan belajar yang mengarah pada strategi pemikiran, strategi pemecahan masalah dan kemampuan berpikir kritis (Schraw, Crippen, & Hartley, 2006).…”